Quantum key distribution deployments provision security margins assuming stationary channels, yet physical devices drift between calibrations. This paper frames adaptive eavesdropping under trusted channel noise and device drift as a constrained Markov decision process, where an attacker selects compact learnt quantum circuits each round while the noise follows an Ornstein–Uhlenbeck process. By bounding the adaptive value between the best fixed circuit and a dynamic-programming upper bound, the authors let operators measure how conservative their margins are and tune recalibration intervals, addressing a key economic barrier to QKD adoption.

This paper studies how to make Quantum Reinforcement Learning (QRL) for combinatorial optimization more robust on near-term quantum devices. Its core idea is APGEM, a LinUCB contextual-bandit controller that dynamically selects among error-mitigation techniques—Zero-Noise Extrapolation, Probabilistic Error Cancellation, Clifford Data Regression, and Readout Error Mitigation—based on a cost-aware utility that balances fidelity gain against shot cost and estimator variance. The work matters because static error mitigation cannot adapt to changing noise regimes or training phases, and adaptive orchestration could improve the reliability of hybrid quantum-classical loops.

How complex can a quantum device's responses become when it runs longer with a fixed internal memory? This paper quantifies this through sequential response capacity—the depth of an adaptively shattered binary tree of experiments—and proves a tight law: for fixed visible and memory dimensions, capacity grows as $\Theta(K\log((K+1)/\gamma))$ in run length $K$ and probability gap $\gamma$. The core construction shows that memory-one qubit phase channels already attain this scaling with exact zero-or-one responses, while fully measured classical processes under the same tester permissions are limited to linear capacity. The result establishes a fundamental information-theoretic separation between coherent quantum control and classical stochastic transitions under a finite memory budget.

Hashimoto and Kawachi study Hamiltonian function learning: predicting $\operatorname{Tr}[f(H)\rho]$ from classical descriptions of a Hamiltonian $H$ and state $\rho$ when the function $f$ is fixed and known. Their core result is a rigorous average-case classical hardness proof for two explicit instances—$f_{\cos,\pi}(\lambda)=\cos(\pi\lambda)$ and $f_{\exp,\beta}(\lambda)=e^{-\beta\lambda}$—obtained by reducing from the hardness of factoring random RSA moduli via Shor's algorithm embedded in projector Hamiltonians. This settles an open problem left by Morohoshi et al. and gives a computational quantum advantage in a quantum machine learning task that has already been demonstrated experimentally on superconducting devices.

This paper investigates whether domain knowledge for quantum code generation should be embedded in model parameters through fine-tuning or provided at inference time via retrieval and agents. Comparing a parameter-specialized Granite-20B baseline against modern general-purpose LLMs (OpenAI, Claude, Gemini) on the Qiskit-HumanEval benchmark, the authors find that inference-time augmentation—particularly agentic execution feedback—outperforms fine-tuning by over 35 percentage points, offering a more maintainable path as quantum SDKs evolve.

Quantum machine learning model selection currently lacks principled guidelines, forcing practitioners to train numerous expensive configurations. This paper introduces QBET (Quantum Bias-Expressivity Toolbox), an unsupervised pre-screening framework that evaluates hybrid quantum-classical transformers using LZ-complexity-based Simplicity Bias (AUC) and Expressivity metrics without gradient descent. The core idea is that architectures with higher AUC (stronger bias toward simple Boolean functions) correlate with better downstream task performance, offering a filter to identify promising quantum attention variants before committing to full training on NISQ devices.

Variational Quantum Classifiers (VQAs) are typically trained in ideal classical simulations, raising concerns about reproducibility on noisy quantum hardware. This paper proposes that the average relative entropy between class distributions combined with transpilation depth predicts noise robustness—introducing the log-DTSAE metric to forecast accuracy degradation without requiring noisy hardware execution. The authors validate this across thousands of models spanning diverse ansatzes, encodings, and simulated backends from IBM, IQM, and IonQ.